Project Summary My research interests include the development of the neonatal enteric microbiome, with a current focus on community-encoded functions such as antibiotic resistance or metabolic functions. I have worked since 2009 in the laboratory of Gautam Dantas, Ph.D.; during this time I have become proficient in a variety of benchtop techniques necessary for work in microbiology and genomics research, have taken classes in statistics and computer programming, and gained experience analyzing increasingly large and complex collections of metagenomic data. I have collaborated with colleagues with expertise in genetics, microbiology, gastroenterology, statistics, and neonatology, resulting in several publications. This proposal includes further didactic training in bioinformatics and computational biology, with a focus on enhancing my skills and knowledge in programming, statistics, and systems biology, as well as a research plan that will complement my formal coursework by providing ample opportunity for me to apply these skills. I will continue to work with my mentors Gautam Dantas, Ph.D., an expert in community-wide functions of human gut and soil bacteria and Phillip Tarr, MD, an physician-scientist who is a leader in the field of pediatric microbiome research, and has a track record of mentoring junior scientists to independence. My oversight committee includes Barak Cohen, Ph.D., an expert in computational and systems biology, William Shannon, Ph.D., an expert in biostatistics, and F. Sessions Cole, MD, an expert in genetics and translational research. The ongoing mentorship of these collaborators with diverse areas of expertise will enhance my didactic and hands-on training and, combined with my clinical experience as an academic neonatologist, will prepare me for an independent career as a physician scientist investigating the impact of the human microbiome on neonatal health. The research project described in this proposal is potentially clinically relevant, as bacterial resistance to all antibiotics urgently threatens human health. The antibiotic resistance genes harbored by the human gut microbiota (fecal resistome) are an epidemiologically important genetic reservoir that can potentially transfer resistance to human pathogens. Understanding the clinical determinants of fecal resistance gene carriage may lead to novel strategies to combat the spread of resistant organisms in human communities. Our recent work has indicated that the fecal resistome of healthy children is far more diverse than previously suspected, and suggests that the fecal resistome is established in early infancy, with infant resistomes being distinct from their mothers' in by 1-2 months of life and developing similarly to their twin siblings'. The goal of this proposal is to test the hypothesis that infant diet significantly influences fecal resistome and fecal microbial community development. Prior work by me and my mentors Dr. Dantas and Dr. Tarr has shown that functional metagenomic selections are an efficient means of broadly sampling pediatric fecal resistomes. We will use high-throughput functional metagenomic selections coupled with next-generation sequencing and a computational pipeline developed in our lab (Parallel Assembly and Annotation of Functional Metagenomic Selections, PARFuMS) to study longitudinal fecal resistome development in the first year of life in infants that are breastfed and formula fed (cow's milk and soy formula both represented) with an emphasis on changes associated with feeding transitions (e.g. from breastmilk to formula, initiating solid food). The samples to be used in this study have already been collected with informed consent for a separate IRB-approved study, and are stored in the Dantas lab. Functional metagenomic selections on a small subset of samples will be used to identify resistance genes that are clinically important and unique to this cohort. We will use the phenotype-linked data generated by functional metagenomic selections to quantify the correlation between specific resistance genes and genetic motifs and resistance phenotypes, which will enhance existing resistance gene databases with functional information and will provide a framework for identifying the most potentially dangerous resistance genes by statistically linking them with undesirable resistance phenotypes. Shotgun sequencing of a much larger sample set will allow high-resolution, quantitative data on the phylogenetic composition of the microbiota and predicted microbiome-encoded functions. We will use this rich dataset to correlate clinical variables with changes in the resistome, as well as to examine the effects of phylogenetic shifts on resistome development. This comprehensive research strategy is novel because it will be, to our knowledge, the first longitudinal study of the effects of early infant nutrition on fecal resistome and microbiome establishment, and will integrate functional metagenomic techniques and whole-genome shotgun sequencing with novel computational strategies developed expressly for longitudinal resistome study. The computational strategies developed for this study will provide a theoretical framework for future longitudinal investigations of other community-wide microbial functions. The work in this proposal will integrate with my clinical experience, my proposed didactic training in bioinformatics, and my mentored experience with metagenomics to prepare me for a career as an independent investigator conducting hypothesis-based research on thedevelopmental properties of the human microbiome and translating this knowledge to impact child health outcomes.